Generally, an image of high quality may contain noise. Larger image data with few unique features may include noise from various sources and would require extensive processing. Therefore, providing input images comprising noisy signals with very few unique features to any image classifier will mislead the image classifier thereby degrading the prediction accuracy of the performance in the image classifier.
Due to the presence of such noisy signals in the image, usage of any type of image classifier for one or more applications such as detecting location finder using image similarity in satellite images, detecting patterns on availability of natural resources based on image similarity patterns in satellite images, detecting image similarities from larger image databases such as income tax department, finance service providers, bank, insurance agencies, forensics, state agencies and the like results in low prediction accuracy. There might be a substantial need for manual intervention for detecting image type and image similarity. In scenarios where large volumes of images need to be processed, more man hours and also more man power may be required. Further, due to the manual intervention many faults and errors may occur in image type and image similarity detection.
Existing techniques find matches and similarities based on distance measure of features present in query image and features present in pre-stored images. However, distance measure identifies semi-identical images or a near match but cannot find the accurate results. In few existing techniques that use image classifiers, due to the presence of noisy signals in the image, use of any image classifier to predict image similarity poses a larger challenge due to low prediction accuracy of the image classifiers.